#Dropout

import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer
#load data
digits=load_digits()
X=digits.data
y=digits.target
y=LabelBinarizer().fit_transform(y)
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3)


#add one more layer and return the output of this layer
def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
    layer_name='layer%s'%n_layer
    with tf.name_scope(layer_name):
      with tf.name_scope('weights'):
        Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='W')
        tf.histogram_summary(layer_name+'/weights',Weights)
      with tf.name_scope('bias'):
        biases=tf.Variable(tf.zeros([1,out_size])+0.1) 
        #biases is recommended not 0
        tf.histogram_summary(layer_name+'/biases',biases)
      with tf.name_scope('Wx_plus_b'):
        Wx_plus_b=tf.matmul(inputs,Weights)+biases
        Wx_plus_b=tf.nn.dropout(Wx_plus_b,keep_prob)   ##dropout
      if activation_function is None:
        outputs=Wx_plus_b
      else:
        outputs=activation_function(Wx_plus_b)
        tf.histogram_summary(layer_name+'/outputs',outputs)
      return outputs

    
#define placeholder for inputs to network
keep_prob=tf.placeholder(tf.float32)
xs=tf.placeholder(tf.float32,[None,64])   #8*8
ys=tf.placeholder(tf.float32,[None,10])


#add output layer
l1=add_layer(xs,64,100,'l1',activation_function=tf.nn.tanh)
prediction=add_layer(l1,100,10,'l2',activation_function=tf.nn.softmax)

#the loss betwwen prediction and real data
cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))     #loss
tf.scalar_summary('loss',cross_entropy)
train_step=tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy)

sess=tf.Session()
merged=tf.merge_all_summaries()

train_writer=tf.train.SummaryWriter("logs/train",sess.graph)
test_writer=tf.train.SummaryWriter("logs/test",sess.graph)

sess.run(tf.initialize_all_variables())


for i in range(500):
    sess.run(train_step,feed_dict={xs:X_train,ys:y_train,keep_prob:1})
    if i%50==0:
       #record loss
       train_result=sess.run(merged,feed_dict={xs:X_train,ys:y_train,keep_prob:1})
       test_result=sess.run(merged,feed_dict={xs:X_test,ys:y_test,keep_prob:1})
       train_writer.add_summary(train_result,i)
       test_writer.add_summary(test_result,i)




